College and Beyond

Preparing Students for a Data-Rich World

This slide deck was presented at East Bay Educational Collaborative Professional Development Center in Warren, Rhode Island on April 12, 2016 where Ruth Krumhansl was a guest speaker. In addition to this presentation, Ruth also led several workshops on EDC Earth Science. The audience was about 45 teachers from all across New England.

Learn more about the workshop.

Building Global Interest in Data Literacy: A Dialogue-Workshop Report

What does it mean to be data literate in the world of “big data”? What should we be teaching students to better prepare them to participate in today’s workforce and society? What steps need to be taken to develop critical data literacy skills in schools? To seek answers to these questions, EDC’s Oceans of Data Institute (ODI) convened an expert panel of both data analysts and educators for a workshop on data literacy.

Big Data, Big Promise

Ruth Krumhansl, Founder of the Oceans of Data Institute (ODI), describes all the ways big data is changing lives today, the challenges that big data brings, and why ODI is working to transform education to include more data-relevant instruction.

"Data will be part of [student's] future and it should be part of their instruction too".

 

Profile of a Big-Data-Enabled Specialist

ODI gathered a panel of experts from the scientific, education, business, and law enforcement fields to develop an occupational profile that describes the specific skills and knowledge needed to compete in a big-data-centered economy. This work is the first of its kind in the field. It is our hope that the results will help inform conversations about college and career readiness at the K–16 education level.

Visualizing Oceans of Data: Educational Interface Design

Large, high-quality online scientific datasets give today’s students the opportunity to work with authentic data and participate in real scientific work. Yet the educational promise of these datasets will not be met without concerted effort. ODI has created two reports to support interface and tool designers in their efforts to create data visualization tools for the classroom.

IPDC Visualization+Data Course

This Visualization+Data Course is backward-designed from college coursework. 

Learn more about the project and additional data courses offered.

Artificial Intelligence Methods in Data Science Curriculum

These open source AI modules were developed through the NSF funded Science+C project. These modules were developed for high school students but can be easily adapted for community college students and may offer a starting point for those of you searching for AI coursework. View this video for an overview of the modules.

Building toward Critical Data Literacy with Investigations of Income Inequality

To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the United States. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S. Census Bureau microdata and the online data analysis tool the Common Online Data Analysis Platform (CODAP).

Resource Collection: Data Science Textbooks, Tools, and Certifications

This collection of resources was generated by Data Pathways Community of Practice members—faculty and administrators from 2-and 4-year institutions building data programs. Learn more about ODI's work to support data programs at 2- and 4-year institutions in this 10-min video.

Differences in How Data is Approached Across Industry & Academia

Massive amounts of data are generated every day on Earth and beyond - upwards of 2.5 quintillion bytes a day, as estimated by CloudTweaks. This offers exciting opportunities to work with data, in both academia and industry. Which setting is a better fit for you? It depends on how you want to work with data. Although data propels work forward in both academic and non-academic settings, academic and industry folks have different needs of data, and therefore different relationships to data.

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